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Coastal areas are often subject to the severe consequences of flooding from intense storms or hurricanes. Increases in coastal development have amplified both flooding intensity and negative impacts for coastal communities. Reductions in pervious land cover and replacement with impervious ones have reduced the amount of ecosystem services. This research examines the services provided by nature-based solutions by applying outputs from Co$ting Nature models into suitability models to quantify ecosystem services along the Texas Coast. Results show that only around 13% of the Houston-Galveston coastal area has relatively high NBS, and nearly ¼ of the area shows relatively low NBS. The majority of the areas lie in the middle, which, due to increases in development, are at particular risk for becoming areas offering low NBS in the future if not treated. Such vulnerability assessment informs future implementation strategies for NBS in coastal communities to protect people and property from flooding.

Recently, spiking neural networks (SNNs) have been widely studied by researchers due to their biological interpretability and potential application of low power consumption. However, the traditional clock-driven simulators have the problem that the accuracy is limited by the time-step and the lateral inhibition failure. To address this issue, we introduce EvtSNN (Event SNN), a faster SNN event-driven simulator inspired by EDHA (Event-Driven High Accuracy). Two innovations are proposed to accelerate the calculation of event-driven neurons. Firstly, the intermediate results can be reused in population computing without repeated calculations. Secondly, unnecessary peak calculations will be skipped according to a condition. In the MNIST classification task, EvtSNN took 56 s to complete one epoch of unsupervised training and achieved 89.56% accuracy, while EDHA takes 642 s. In the benchmark experiments, the simulation speed of EvtSNN is 2.9–14.0 times that of EDHA under different network scales.

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